Timing of initiation of anti-retroviral therapy predicts post-treatment control of SIV replication

One approach to ‘functional cure’ of HIV infection is to induce durable control of HIV replication after the interruption of antiretroviral therapy (ART). However, the major factors that determine the viral ‘setpoint’ level after treatment interruption are not well understood. Here we combine data on ART interruption following SIV infection for 124 total animals from 10 independent studies across 3 institutional cohorts to understand the dynamics and predictors of post-treatment viral control. We find that the timing of treatment initiation is an important determinant of both the peak and early setpoint viral levels after treatment interruption. During the first 3 weeks of infection, every day of delay in treatment initiation is associated with a 0.22 log10 copies/ml decrease in post-rebound peak and setpoint viral levels. However, delay in initiation of ART beyond 3 weeks of infection is associated with higher post-rebound setpoint viral levels. For animals treated beyond 3 weeks post-infection, viral load at ART initiation was the primary predictor of post-rebound setpoint viral levels. Potential alternative predictors of post-rebound setpoint viral loads including cell-associated DNA or RNA, time from treatment interruption to rebound, and pre-interruption CD8+ T cell responses were also examined in the studies where these data were available. This analysis suggests that optimal timing of treatment initiation may be an important determinant of post-treatment control of HIV.

Table A: Summary of the cohorts.

Fig. A :
Fig. A: Dynamics of the post-rebound setpoint viral load broken down by cohort.# Timeweighted set-point viral loads were averaged over shorter time intervals for some animals.Descriptive statistics and statistical comparisons of the groups treated on different days are summarised in the tables below:

Fig C: Prediction of setpoint viral load using a 2 -
Fig C: Prediction of setpoint viral load using a 2-variable model.A. For animals treated before day 20, timing of ART initiation is the strongest predictor of postrebound setpoint VL (Fig 2D, main text).Adding data on the viral load at treatment initiation into the model did not significantly improve the fit (adjusted R 2 =0.15 vs 0.13, p-value comparing model with day only and (day + VL) = 0.17).B. For animals treated after day 20, viral load at treatment initiation is a good predictor of rebound setpoint viral load (Fig 2E, main text).Adding day of treatment as a factor significantly improves prediction (adjusted R 2 =0.51 vs adjusted R 2 =0.4,p-value comparing VL only with (VL + day) models <0.0001).

Fig D :
Fig D: Relationship between early post-rebound viral parameters and later setpoint viral loads.A-C.Relationship between post-rebound peak and setpoint viral load.

Fig E :
Fig E: Latent proviral reservoir by cohorts.(A, B) SIV DNA copies per million PBMC and (C, D) SIV RNA copies per million PBMC are negatively correlated with post-rebound control in groups from Cohort 1 (A, C) (Spearman r= -0.59, p=0.03 for DNA, and r=-0.77,p=0.002 for RNA), suggesting that larger reservoir size was associated with lower post-rebound setpoint viral load.However, no significant correlation was observed in data from Cohort 2 (B, D).

Fig F :
Fig F: Duration of post-rebound viral control.(A-C) The proportion of animals maintaining viral loads below 10,000 copies per ml over time post-rebound separated by cohort.The duration of control is significantly different between ART initiation groups in 2 of the 3 cohorts (p-values for the Log-rank test are shown in the figures).(D-F) The Duration of post-rebound viral control below 1,000 copies per ml.(D) The proportion of animals maintaining viral control below 1,000 copies per ml is not significant (p-values for the Logrank test are shown in the figures).(E)Animals that have a low peak of the viral load during early rebound are more likely to maintain viral control below 1,000 copies per ml.(F) There is a trend for low viral growth rate during posttreatment rebound to be associated with longer-term control of post-rebound viral loads (not significant when considering four different levels of growth as shown, p = 0.073).However, comparing groups with the growth rate <1 and ≥1, we observed significant differences in the duration of control -p-value = 0.0085.Coloured stars indicate groups where all animals had viral loads greater than 10,000 copies per ml (A) or 1,000 copies per ml (E) at day 30 post-detection.In order to avoid the initial post-rebound peak of viral load in the analysis of the duration of viral control, the first 30 days after detection of virus are ignored (shaded grey).

Fig G :
Fig G: Relationship between exposure to virus pre-treatment, duration of treatment, and postrebound setpoint viral load according to the model defined by formula (3).Increasing exposure to virus before treatment leads to an initial decrease in post-rebound setpoint viral load (consistent with the priming of immune responses).However, further exposure to virus before treatment leads to increasing post-rebound setpoint viral load (consistent with immune exhaustion and/or viral escape).Prolonged treatment is associated with increased setpoint viral load post-rebound, which can be explained by the decline of immune memory and/or immune exhaustion due to exposure to low levels of viral antigen.We assume that setpoint at primary infection corresponds to the point when the day of treatment is equal to 0.

Fig H :
Fig H: No statistically significant difference in the studied parameters in vaccinated (cohort 2) or treated with immune checkpoint blockade (cohort 3) subgroups and control subgroups.Cohort 2, macaques treated with ART on days 6-9 and 42 (A-H) and cohort 3, macaques treated with ART on day 60 (I-M).No statistically significant difference between the median of vaccinated (cohort 2) or treated with ICB (cohort 3) and control subgroups by Mann-Whitney test with respect to the parameters discussed in the main text such as the rebound setpoint viral load (A, D, I), rebound peak viral load (B, E, J), rebound growth rate (C, F, K), SIV cell-associated DNA and RNA measured before ART interruption in cohort 2 (G, H) or on day 28 post-treatment in cohort 3 (L, M).

The simultaneous fit of function (2) to log10 setpoint and peak viral load after rebound.
Function (2) fitted to both setpoint and peak viral loads with the same parameters except for the maximal value, b0 suggests that peak is approximately 10-fold higher than setpoint independent of the timing of ART initiation (shift on Y-axes is equal to 0.98 log10 c/ml).
A. Cohort 1. Function (2) fitted with independent parameters for both datasets does not have a statistically better fit (F-Test's p-value = 0.91).The best-fit parameters for both models are in TableBModel

Table B : The best-fit parameters of the piecewise regression (equation (2)) fitted to the different datasets.
The model with different parameters for each dataset does not fit better than the model where only b0 -s are different for setpoint and peak dataset, as determined by the F-test, p-value =0.91.

Table C : Corrected AIC for models that differ between datasets of setpoint and peak viral load by parameters
shown.The model where the only different parameter is b0 has better fit according to AICc (Fig B and TableB).

Table D . Best-fit parameter for the linear mixed effect model analysis of the relationship between the duration of treatment and the viral load setpoint at the rebound
. A comparison of models with and without random effects for slopes shows that adding random effects does not improve the fit suggesting a similar rate of decay of protection among the groups.